Pedro F. Felzenszwalb Research Group

Brown University

How can algorithms detect and localize people and objects robustly in natural images with deformations and occlusions? The lab developed and evaluated a deformable part model for person detection in benchmark challenges to improve object localization under variation. Researchers design and analyze algorithms connecting computer vision with optimization and statistics to enable reliable image restoration and segmentation. The group implements method combinations from machine learning, signal processing and natural language processing to address mid- and high-level vision tasks. The team releases papers, talk slides and code to document methods and reproducible evaluations.